New year’s resolutions are a good way to define a long-term roadmap for the year ahead because they can help us set priorities and organize our time wisely. That’s why I decided to join DataCamp’s initiative, and here are my Data Science resolutions for 2018.

One of my main goals is to spread the word about Scala within the Data Science community. The more I learn about the different features that Scala offers to users, the more I want to share this knowledge and my excitement about it with my colleagues all around the world. In the past, I have discussed the potential of Scala as a new language for Data Science at conferences in France and Russia, and this year I plan to publish some articles and give some talks to show how easy and efficient it can be to conduct Data Science in Scala. In 2018 I hope to see the Scala community of data scientists grow!

While I consider Scala to be the best language for doing Data Science, I have to admit that Python holds an important position in this domain. There are multiple reasons not to close the door on Python, and last year I started to use it more with machine learning libraries such as Keras, PyTorch, and Prophet. While it was very intuitive to experiment in Python, it was hard to build complex projects that were easy to maintain in the long term. However, difficult doesn’t mean impossible. Indeed, with some supplemental tools and techniques, one can overcome the drawbacks of imperative programming and dynamic typing. Thus, another resolution for me is to improve existing and future Python projects with functional programming concepts and different tools for static type checking.

My everyday work doesn’t intersect with computer vision, so I plan to start some side projects in this domain. This is an exciting field that encompasses various challenges in image and video processing, and I’d like to learn and practice more with them this year.

Data Science is becoming more and more ubiquitous, and as practitioners we have to take some responsibility to ensure that Data Science transforms society in the right way, meaning that everyone can benefit from these transformations, the privacy and equal rights of everyone are respected, and the mechanisms of the transformations are transparent and clear to everyone. That’s why, this year, I’m not only going to focus on the technical sides of Data Science; I’m also going to focus on its ethical, legal, and educational aspects. I believe Data Science has great power, but it can be either destructive or constructive, and it’s up to us to put it and keep it on the right track.

Finally, in order to achieve much of what I mentioned above, I plan to take considerable time to master effective communication, both professional and interpersonal. One of the hardest skills in a domain as technical as Data Science is communication, even if it is so often, wrongly, taken for granted. In everything we do—from presenting your new algorithm to commercial managers or the scientific community to explaining statistical methods to undergraduate students or introducing a new machine learning library to your teammates to reviewing pull requests or just responding to a client’s email—communication is critical to the variety of data scientists’ professional activities and, it plays a crucial role in their success.